Recommender systems are well-known in the art as a form of intelligent software agent, which operates to provide specific “recommendations” to users (typically consumers) of goods and/or services that are predicted to be of interest. A number of commercial implementations can be found in the prior art, including at Internet commerce sites operated by Yahoo!, Amazon, EBay, and Netflix, and by electronic program managers, such as TiVo.
In general, recommender systems operate using one or more basic methodologies, which can be classified basically as collaborative filtering (CF) based, content based (CN) or a hybrid of the two. In the case of CF, the user's interests are determined through a variety of mechanisms, including demographic profiling, explicit and implicit data gathering, ratings for items, etc., to generate a profile. The user is then compared against other users with a similar profile, to see if there are items which the user may not be aware of, but which are rated highly by others like him/her. Thus, the comparison and analysis for collaborative filtering is basically between individuals.
In the content based filtering schemes, the primary focus is similarity between items, with little regard for the types of persons who prefer the item. In other words, the recommender merely attempts to correlate pairs of items, so that, for example, if two items are highly correlated (i.e. meaning that the selection of one is often accompanied by the selection of the other) the system can inform the user of such fact. Thus, the comparison is primarily between items.
One aspect of recommender systems that is not addressed in the prior art is a mechanism for biasing or influencing recommendations based on some external parameter that is unrelated to the individual's correlation with other individuals, and unrelated to the characteristics of the item. For example, it is well-known in the art to implement so-called “pay for placement” features and polices in, among other places, Internet based search engines. In some cases, a company can “buy” a high ranking for one or more keywords, so that, for instance, when a user visits a particular website and enters a query into a search engine (say on topic A), such site provides responsive material to such user from sources in accordance with which companies have paid a premium to be associated with topic A.
Thus, Apple Computer company may reserve and pay consideration to a search engine provider to make certain that any queries involving the word “apple” will always contain (and in some cases highlight or showcase) a “hit” to web page, ad or other content from Apple. In this fashion, a company can influence or bias a search engine to produce results that favor presentation of content from one source over another, regardless of any other correlations that may exist to the underlying query.
Accordingly, while such systems exist for influencing a user's selection of content or purchases based on keyword searches, there is an unmet need for a system or method for influencing a recommender system in a similar fashion. Clearly it would be desirable if a company could influence a purchaser while he/she is reviewing content, such as by recommending content that is characterized by some quality such as the origin of the content. In this way, originators/suppliers of such content (be it goods, services, etc.) can benefit from a treatment within a recommender system environment that is similar to that afforded to the aforementioned search engine applications. Again, since the origin of a product/service is not considered by a typical recommender system, the prior art provides no mechanism for this type of functionality.